Application of Whole-Genome Prediction Methods for Genome-Wide Association Studies: a Bayesian Approach

Abstract Text:

This paper discusses how Bayesian multiple-regression methods that are used for whole-genome prediction
can be adapted for genome-wide association studies (GWAS). It is argued
here that controlling the posterior type I error rate (PER) is more
suitable than controlling the genomewise error rate (GER) for
controlling false positives in GWAS. It is shown here that under ideal
conditions, PER can be controlled by using Bayesian posterior
probabilities that are easy to obtain. Computer simulation was used to
examine the properties of this Bayesian approach when the ideal
conditions were not met. Results indicate that useful inferences can
be made using Bayesian posterior probabilities.

Keywords:

genome-wide association studies
Bayesian multiple-regression analyses